While trying to detect dismounts and other slow-moving targets, a radar platform, such as a ground moving target indication (GMTI) radar minimum detectable velocity, is limited by the radar dwell duration azimuth-Doppler extent of the clutter. The problem is exacerbated by factors such as short duration dwells, wind-blown ground clutter, rain clutter, and bird-flock clutter and radio frequency interference (RFI). It can be difficult to separate target from clutter returns when the clutter is spread in Doppler, in which target and clutter returns overlap in Doppler. The clutter (and other non-target signals) can be Doppler spread due to factors such as: radar platform motion; the nature of the clutter, such as whether it is wind blow, rain, bird flock, sea, etc.; or other factors such as miscalibration and RFI. The target trackers or clutter maps can be overwhelmed by a very large number of clutter-hit detections (especially in air-to-ground modes). Furthermore, for a slow radial velocity target, it becomes increasingly difficult to distinguish the target from the non-stationary clutter radar return signal.
A traditional technique to detect endo-clutter targets is Space-Time Adaptive Processing (STAP). The STAP technique combines adaptive beamforming and adaptive Doppler filtering into a single 2-D algorithm to yield 2-D detection weights for a target at each candidate azimuth and Doppler. A primary disadvantage of this method is that determination of adaptive weights requires stationary interference and training data that adequately captures the space-time correlation of such interference. Performance of STAP may be deleteriously impacted by signal interference that is difficult to train on, such as non-stationary clutter and terrain bounced interference. Furthermore, the STAP method requires large number of radar return snapshots for training.